Genomic investigation of etiologic heterogeneity: methodologic challenges
Begg, Colin B
Seshan, Venkatraman E
Zabor, Emily C
Maranchie, Jodi K
Nielsen, Matthew E
Rathmell, W Kimryn
Karam, Jose A
Hakimi, A Ari
Hsieh, James JNote: Order does not necessarily reflect citation order of authors.
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CitationBegg, C. B., V. E. Seshan, E. C. Zabor, H. Furberg, A. Arora, R. Shen, J. K. Maranchie, et al. 2014. “Genomic investigation of etiologic heterogeneity: methodologic challenges.” BMC Medical Research Methodology 14 (1): 138. doi:10.1186/1471-2288-14-138. http://dx.doi.org/10.1186/1471-2288-14-138.
AbstractBackground: The etiologic heterogeneity of cancer has traditionally been investigated by comparing risk factor frequencies within candidate sub-types, defined for example by histology or by distinct tumor markers of interest. Increasingly tumors are being profiled for molecular features much more extensively. This greatly expands the opportunities for defining distinct sub-types. In this article we describe an exploratory analysis of the etiologic heterogeneity of clear cell kidney cancer. Data are available on the primary known risk factors for kidney cancer, while the tumors are characterized on a genome-wide basis using expression, methylation, copy number and mutational profiles. Methods: We use a novel clustering strategy to identify sub-types. This is accomplished independently for the expression, methylation and copy number profiles. The goals are to identify tumor sub-types that are etiologically distinct, to identify the risk factors that define specific sub-types, and to endeavor to characterize the key genes that appear to represent the principal features of the distinct sub-types. Results: The analysis reveals strong evidence that gender represents an important factor that distinguishes disease sub-types. The sub-types defined using expression data and methylation data demonstrate considerable congruence and are also clearly correlated with mutations in important cancer genes. These sub-types are also strongly correlated with survival. The complexity of the data presents many analytical challenges including, prominently, the risk of false discovery. Conclusions: Genomic profiling of tumors offers the opportunity to identify etiologically distinct sub-types, paving the way for a more refined understanding of cancer etiology. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-138) contains supplementary material, which is available to authorized users.
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